UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling

• Published in: arXiv.org
• Main Authors: Yang, Linghao, Wu, Yanmin, Deng, Yu, Tian, Rui, Hu, Xinggang, Ma, Tiefeng
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 02.10.2023
• Subjects: Datasets; Interactive systems; Modelling; Motion simulation; Moving object recognition; Optimization; Optimization models; Parameterization; Perception; Simultaneous localization and mapping; State estimation; Static objects; Tracking; Virtual reality; Weight reduction
• ISSN: 2331-8422

Tracking and modeling unknown rigid objects in the environment play a crucial role in autonomous unmanned systems and virtual-real interactive applications. However, many existing Simultaneous Localization, Mapping and Moving Object Tracking (SLAMMOT) methods focus solely on estimating specific object poses and lack estimation of object scales and are unable to effectively track unknown objects. In this paper, we propose a novel SLAM backend that unifies ego-motion tracking, rigid object motion tracking, and modeling within a joint optimization framework. In the perception part, we designed a pixel-level asynchronous object tracker (AOT) based on the Segment Anything Model (SAM) and DeAOT, enabling the tracker to effectively track target unknown objects guided by various predefined tasks and prompts. In the modeling part, we present a novel object-centric quadric parameterization to unify both static and dynamic object initialization and optimization. Subsequently, in the part of object state estimation, we propose a tightly coupled optimization model for object pose and scale estimation, incorporating hybrids constraints into a novel dual sliding window optimization framework for joint estimation. To our knowledge, we are the first to tightly couple object pose tracking with light-weight modeling of dynamic and static objects using quadric. We conduct qualitative and quantitative experiments on simulation datasets and real-world datasets, demonstrating the state-of-the-art robustness and accuracy in motion estimation and modeling. Our system showcases the potential application of object perception in complex dynamic scenes.